(1) Field of Invention
The present invention relates to a system for improving the performance of a multi-class classifier in a visual object detection system and, more particularly, to a system for improving the performance of a multi-class classifier in a visual object detection system using particle swarm optimization to estimate threshold-offset.
(2) Description of Related Art
Multi-class classification involves assigning one of several class labels to an input object. Typical approaches to multi-class visual object recognition utilize the precision-recall curve to depict recognition performance. The Precision-Recall curve is a widely used tool to evaluate the performance of scoring functions in discriminating between two populations. The use of the Precision-Recall curve stems from focusing on the problem of image retrieval as opposed to surveillance. The separate domains of image retrieval and surveillance consider different types of errors as more or less tolerable. For instance, the number of images of the same target that appears before a surveillance object recognition system is more numerous and, as a partial result, false-positive rates of whole percentage points are not as tolerable as they are in image retrieval.
Multi-class visual object recognition for surveillance has been an active research problem, but much of the progress has been made on tracking and the 2-class problem, as opposed to the multi-class problem. For the multi-class problem, the community appears waiting for the results to emerge from the image retrieval research. Because of this and because multi-class visual object recognition has defined different types of errors as tolerable, the issue of tuning the multi-class classification to suit the surveillance problem has not been investigated in depth. Thus, a continuing need exists for a solution to the problem of multi-class visual object recognition for surveillance.